code: def test_python_export_from_python(dataset, iterations):
      train_pool, test_pool = _get_train_test_pool(dataset)
  
      model = CatBoost(
          {
              "iterations": iterations,
              "random_seed": 0,
              "loss_function": __get_train_loss_function(dataset),
          }
      )
      model.fit(train_pool)
      pred_model = model.predict(
          test_pool, prediction_type="RawFormulaVal"
      )
  
      model_py = yatest.common.test_output_path("model.py")
      model.save_model(model_py, format="python", pool=train_pool)
  
      is_multiclass_model = (
          __get_train_loss_function(dataset) == "MultiClass"
      )
  
      scope = {}
      exec(open(model_py).read(), scope)  # noqa
      pred_python = _predict_python_on_test(
          dataset,
          scope[
              "apply_catboost_model_multi"
              if is_multiclass_model
              else "apply_catboost_model"
          ],
      )
  
      assert _check_data(pred_model, pred_python)
  
file_path: catboost/catboost/libs/model/model_export/ut/test.py
imports: import numpy
  import os
  import pytest
  import re
  import yatest
  from catboost import Pool, CatBoost, CatBoostClassifier
  from catboost_pytest_lib import data_file, load_pool_features_as_df
label_desc: Use image node_id 10 for calling a global function with example usage: test_python_export_from_python(dataset, iterations) without return types